The Influences of Atlantic Sea Surface Temperature Anomalies on the ENSO-Independent Interannual Variability of East Asian Summer Monsoon Rainfall

Ying Yang aKey Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Zhiwei Zhu aKey Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
bState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Xinyong Shen aKey Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Leishan Jiang aKey Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Tim Li cInternational Pacific Research Center and Department of Atmospheric Sciences, University of Hawai‘i at Mānoa, Honolulu, Hawaii
aKey Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Abstract

As the most dominant tropical climate mode on the interannual time scale, El Niño–Southern Oscillation (ENSO) is suggested to significantly influence the interannual variation of East Asian summer monsoon rainfall (IEASMR). However, the leading mode of IEASMR remains almost untouched when the impacts of preceding ENSO events are linearly removed, suggesting the existence of alternative impact factors and predictability sources of IEASMR. After removing the impact of ENSO, the sea surface temperature anomalies (SSTAs) over both the tropical Atlantic and extratropical North Atlantic are found to be related to IEASMR through atmospheric teleconnections. Positive SSTA over the tropical Atlantic could induce tropical diabatic heating, which triggers an equivalent barotropic Rossby wave train emanating from the Atlantic, going across the Eurasian continent, and ending with a cyclonic anomaly over northeast Asia. The tropical diabatic heating could also induce western North Pacific anomalous anticyclone via tropical routes. The dipole SSTA pattern with cooling in the west and warming in the east over the extratropical North Atlantic induces local circulation anomalies through heat flux exchange, which could further perturb a Rossby wave train with a cyclonic anomaly over northeast Asia, thus modulating IEASMR. Numerical experiments with prescribed atmospheric heating associated with Atlantic SSTAs could realistically reproduce these teleconnections toward IEASMR. By adding the predictability sources of Atlantic SSTAs, the seasonal hindcast skills of IEASMR could be significantly improved over both the tropical western North Pacific and subtropical land regions such as central China and Japan.

Significance Statement

The purpose of this article is to identify the alternative impact factors of the interannual variation of East Asian summer monsoon rainfall (IEASMR), after removing the impact of ENSO, considering the limited contribution of ENSO to the variances of IEASMR. Here, we find that the Atlantic sea surface temperature anomalies (SSTAs) play a considerable role in driving IEASMR. The impact of ENSO on IEASMR is mainly confined to the tropical western North Pacific, while the Atlantic SSTAs influence rainfall over subtropical East Asia and the tropical western North Pacific through both tropical and extratropical routes. The results unravel the important roles of Atlantic SSTAs in driving ENSO-independent IEASMR, which will have a large implication for the seasonal prediction of East Asian summer climate.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhiwei Zhu, zwz@nuist.edu.cn

Abstract

As the most dominant tropical climate mode on the interannual time scale, El Niño–Southern Oscillation (ENSO) is suggested to significantly influence the interannual variation of East Asian summer monsoon rainfall (IEASMR). However, the leading mode of IEASMR remains almost untouched when the impacts of preceding ENSO events are linearly removed, suggesting the existence of alternative impact factors and predictability sources of IEASMR. After removing the impact of ENSO, the sea surface temperature anomalies (SSTAs) over both the tropical Atlantic and extratropical North Atlantic are found to be related to IEASMR through atmospheric teleconnections. Positive SSTA over the tropical Atlantic could induce tropical diabatic heating, which triggers an equivalent barotropic Rossby wave train emanating from the Atlantic, going across the Eurasian continent, and ending with a cyclonic anomaly over northeast Asia. The tropical diabatic heating could also induce western North Pacific anomalous anticyclone via tropical routes. The dipole SSTA pattern with cooling in the west and warming in the east over the extratropical North Atlantic induces local circulation anomalies through heat flux exchange, which could further perturb a Rossby wave train with a cyclonic anomaly over northeast Asia, thus modulating IEASMR. Numerical experiments with prescribed atmospheric heating associated with Atlantic SSTAs could realistically reproduce these teleconnections toward IEASMR. By adding the predictability sources of Atlantic SSTAs, the seasonal hindcast skills of IEASMR could be significantly improved over both the tropical western North Pacific and subtropical land regions such as central China and Japan.

Significance Statement

The purpose of this article is to identify the alternative impact factors of the interannual variation of East Asian summer monsoon rainfall (IEASMR), after removing the impact of ENSO, considering the limited contribution of ENSO to the variances of IEASMR. Here, we find that the Atlantic sea surface temperature anomalies (SSTAs) play a considerable role in driving IEASMR. The impact of ENSO on IEASMR is mainly confined to the tropical western North Pacific, while the Atlantic SSTAs influence rainfall over subtropical East Asia and the tropical western North Pacific through both tropical and extratropical routes. The results unravel the important roles of Atlantic SSTAs in driving ENSO-independent IEASMR, which will have a large implication for the seasonal prediction of East Asian summer climate.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhiwei Zhu, zwz@nuist.edu.cn

1. Introduction

The East Asian summer monsoon (EASM) is one of the most prominent monsoon systems around the globe (Lau 1992; Wang and Ding 2006), and it provides the major water resources that support the most densely populated countries (Kubota et al. 2016). Given that the spatial–temporal distribution of EASM rainfall can considerably influence the local agriculture, economy, and ecosystems (Huang et al. 2003; He et al. 2007), it is imperative to understand its dynamic origins and improve the accuracy of its seasonal prediction.

As the most dominant climate mode on the interannual time scale, El Niño–Southern Oscillation (ENSO) is suggested to significantly impact the interannual variation of EASM rainfall (IEASMR) through inducing and maintaining the western North Pacific anomalous anticyclone (WNPAC; Wang et al. 2003; Wu et al. 2003; Wang and Li 2004; Li and Wang 2005). The warm sea surface temperature anomalies (SSTAs) over the equatorial central/eastern Pacific induce WNPAC by a Pacific–East Asian teleconnection through the wind–evaporation–SST feedback (Zhang et al. 1996; Wang et al. 2000, 2003; Stuecker et al. 2015). The ENSO-related Indian Ocean Basin–wide SST warming, acting as a capacitor, also supports WNPAC (Wang et al. 2009; Xie et al. 2009, 2015). Recent studies (Wu et al. 2017a,b) suggest that the WNPAC is also established through the wind–moist enthalpy advection–convection feedback induced by ENSO.

Besides ENSO, many other factors could also impact IEASMR. In the tropical region, the tropical Atlantic (TA) SSTA modulates local convection and induces circulation anomalies over the western North Pacific via both the Rossby wave process (Ham et al. 2013; Chen et al. 2015) and Kelvin wave process (Rong et al. 2010; Jiang and Li 2021), thus influencing IEASMR (Lu and Dong 2005; Jiang and Li 2021). In the subtropical region, the persistent sensible and latent heating over the Tibetan Plateau from spring to summer could affect the downstream, impacting IEASMR (Hsu and Liu 2003; Hu and Duan 2015). The variations of South Asian high (Wei et al. 2013, 2015) and the westerly jet (Sampe and Xie 2010; Chowdary et al. 2019; Wang et al. 2020) could also jointly affect IEASMR. Meanwhile, the Silk Road pattern (Wang et al. 2000; Lu et al. 2002) propagating from North Africa induces EASM rainfall anomaly (Wu 2017; Wang and Wang 2018; Hu et al. 2020). Over the mid- to high latitudes, the North Atlantic Oscillation could induce a tripole SSTA pattern over the North Atlantic, which excites downstream teleconnections and modulates IEASMR (Ciasto and Thompson 2004; Wu et al. 2012; Sun and Wang 2012). The decreased Arctic sea ice could induce a Rossby wave train from northern Europe (Zhao et al. 2004). With the wave train propagating southeastward, the IEASMR can be impacted (Wu et al. 2009; Guo et al. 2014). In addition, the Arctic Oscillation could lead to the meridional shift of the westerly jet stream over East Asia and thus influence IEASMR (Gong and Ho 2003; Gong et al. 2011).

The abovementioned studies suggested the existence of the impact factors of IEASMR besides ENSO. However, these studies did not exclude ENSO’s impact when searching for other impact factors, so it is unclear whether these factors are influenced by ENSO or not. Recently, increasing evidence has indicated that the relationship between ENSO and IEASMR is unrobust and unstable (Wang et al. 2008, 2017, 2020). Based on the 60-yr observation, Wang et al. (2017) concluded that only strong El Niño events can persistently enhance rainfall over East Asia to the ensuing summer. Meanwhile, the relationship between ENSO and IEASMR also shows an interdecadal change after the late 1970s (Wu and Wang 2002; Ye and Lu 2011). In the summer of 2020, record-breaking monsoon rainfall occurred over the Yangtze River basin (i.e., 2020 prolonged mei-yu) without a preceding ENSO signal (Liu et al. 2020; Pan et al. 2021; Zheng and Wang 2021). These findings have motivated us to reinvestigate to what extent ENSO can explain IEASMR.

As will be presented in this paper, the variance of IEASMR explained by ENSO is limited. Therefore, the main scientific issues we aim to address here are the following two questions: what are the independent impact factors and predictability sources of IEASMR after removing the impact of ENSO? What are the physical processes through which these independent impact factors drive IEASMR? The answers to these questions are beneficial for further understanding the dynamic origins of IEASMR and improving its prediction skill.

The remainder of this paper is organized as follows: section 2 describes the data, methods, and model used in this study. Section 3 reveals the limited variance of IEASMR explained by ENSO and examines the leading mode of the ENSO-independent IEASMR and its associated dynamic and thermodynamic fields. The physical processes underlying the Atlantic forcings on IEASMR, which are independent of ENSO, are investigated via observational diagnosis and numerical simulations in section 4. Section 5 provides the quantified contributions of the Atlantic forcings to seasonal prediction of IEASMR. Section 6 presents the summary and discussion.

2. Data, method, and model

The datasets used in this study include 1) the monthly wind field, geopotential height field, and air temperature field data with a horizontal resolution of 1° × 1° from National Centers for Environmental Prediction (NCEP) reanalysis (Kalnay et al. 1996); 2) the monthly precipitation data with a horizontal resolution of 2.5° × 2.5° from Global Precipitation Climatology Project (GPCP; Adler et al. 2003); and 3) monthly sea surface temperature data with a horizontal resolution of 1° × 1° from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (Rayner et al. 2006). All the datasets cover the period from 1979 to 2020.

To focus on the interannual variability, the interdecadal component (the 9-yr running mean) is excluded before all the calculations. Because the impact of ENSO on East Asian climate maintains from winter to the following summer, the summer-mean fields are regressed onto the previous winter-mean (ENSO mature phase) Niño-3.4 index to calculate the ENSO-related summer-mean fields. In the present study, the Niño-3.4 index (hereafter EN) is defined as the December–February (DJF) mean SSTA over the tropical central and eastern Pacific (5°S–5°N, 120°–170°W; Rasmusson and Carpenter 1982). Three steps are taken to extract the IEASMR independent of ENSO (Hu et al. 2005; Huangfu et al. 2019). First, the June–August (JJA) mean precipitation (in ENSO decaying summer) is regressed onto the EN to extract the summer precipitation associated with the preceding ENSO (Reg_EN). Then, the ENSO-related precipitation (Prec_EN) is reconstructed as the product of the EN and the corresponding regressed rainfall fields (Prec_EN = EN × Reg_EN). Finally, the IEASMR independent of ENSO is obtained using the original summer precipitation field minus the ENSO-related summer-mean precipitation field (Prec_noEN = Prec − Prec_EN). Note that all the data are not detrended and the regression remains consistent with or without detrend.

The empirical orthogonal function analysis (EOF) is applied on summer-mean (JJA) precipitation over East Asia (10°–50°N, 100°–150°E) to extract the leading mode of IEASMR, and the corresponding principal component (PC) can be used to describe the temporal evolution of the EOF pattern. A two-tailed Student’s t test (Orloff and Bloom 2014) is adopted for the significance test of regression analysis.

The Rossby wave activity flux is calculated to explore the origins of IEASMR and the associated atmospheric teleconnections. The formula of Rossby wave activity flux (Takaya and Nakamura 2001) is as follows:
W=12|U¯|[u¯(ψx2ψψxx)+υ¯(ψxψyψψxy)u¯(ψxψyψψxy)+υ¯(ψx2ψψyy)].
Among them, ψ represents the flow function, U = (u, υ) is the horizontal wind, and W refers to the two-dimensional Rossby wave activity flux. The overbar represents the climatological mean, and the prime represents the anomaly.

To demonstrate the physical mechanism of IEASMR independent of ENSO, an anomaly atmospheric general circulation model (AGCM; Held and Suarez 1994) is employed. Based on the GFDL’s global spectrum dry AGCM, this global spectral model uses sigma (σ = p/ps) as its vertical coordinate, and it has a T42 Gaussian grid horizontal resolution. The basic equations in this intermediate model include the momentum equation, the continuity equation, the thermal dynamic equation, and the hydrostatic equation. The long-term JJA mean of the NCEP–NCAR reanalysis is used as a realistic summer-mean state in the model. All the experiments are integrated for 60 days, and the averaged output in the last 20 days is considered as the equilibrium state (Zhu and Li 2016). This model has been widely used to investigate the formation of various atmospheric teleconnection patterns (Zhu and Li 2018; Lu et al. 2020; Zhu et al. 2023).

3. The leading modes of IEASMR

Before extracting the leading mode of IEASMR, we first examine to what extent the preceding ENSO can explain IEASMR. Figure 1 shows the ENSO-related fractional variance (calculated by the ratio of ENSO-related rainfall variance to the total rainfall variance) of IEASMR. During the summer following ENSO episodes, the ENSO-related rainfall only contributes to limited variance over East Asia. Specifically, the fractional variance is 10.36% over the tropical western North Pacific and 6.32% over the South China Sea with a maximum located over Luzon Island of the Philippines. It decreases to 2.33% over subtropical eastern China with a maximum in central China and 4.77% over the subtropical western North Pacific with a maximum in the east of Japan.

Fig. 1.
Fig. 1.

Distribution of the fractional variance of rainfall reconstructed by EN index over East Asia. Purple lines represent the Yangtze River and Yellow River. The gray dashed lines are 27°N and 127°E, which divide the four subregions of the East Asian monsoon domain with their averaged variances shown in the top left of each subregion.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

Given that the Indian Ocean Basin mode (IOBM) warming/cooling is considered the capacitor of previous ENSO event (Xie et al. 2009, 2015; Figs. S1a,b in the supplemental material), the fractional variance of rainfall associated with the IOBM (defined as the June–August mean SSTA over 20°S–20°N, 40°–100°E) is also calculated. It is found that IOBM-related rainfall during boreal summer also contributes to limited variance of the total rainfall over East Asia (Fig. S2), which is even smaller than that of EN. Therefore, consistent with the result of Wang et al. (2017), ENSO contributes to a small portion of IEASMR, particularly for the East Asian subtropical land region.

Since ENSO has limited contribution to IEASMR, then will the leading mode of IEASMR remain similar after removing ENSO’s impact? Figure 2a shows the spatial pattern of the first EOF mode of IEASMR (the original interannual summer-mean precipitation field), which is significant and well separated from other modes (North et al. 1982). The leading mode is characterized by a north–south dipole pattern with negative precipitation anomalies over the western North Pacific and positive precipitation anomalies over the Yangtze River basin, the Korean Peninsula, and southern Japan. This meridional rainfall pattern over East Asia corresponds to the existence of the WNPAC and the northeast Asian anomalous cyclone (NEAC). On the southern flank of WNPAC, suppressed precipitation is observed over the tropical western North Pacific due to the inhibited tropical convection. On the northwestern flank of WNPAC and the southwestern flank of NEAC, enhanced precipitation appears from the Yangtze River basin to southern Japan because of the southwesterly moisture transportation and water vapor convergence. It is noteworthy that the centers of the negative/positive precipitation anomalies are consistent with the maximum fractional variances of IEASMR explained by ENSO (Fig. 1). Since the WNPAC is mainly linked with ENSO (Wang et al. 2003; Xie et al. 2009), larger fractional variance explained by ENSO appears south of 27°N, where only the WNPAC dominates. On the contrary, smaller fractional variance explained by ENSO can be found north of 27°N because this region is not only controlled by the WNPAC but also influenced by the NEAC (Fig. 2a).

Fig. 2.
Fig. 2.

(a) The spatial pattern of the first EOF mode of boreal summer precipitation (shading; mm day−1) over East Asia from 1979 to 2020. (b) As in (a) but for IEASMR independent of EN. (c) The standardized principal component (black line) of the first EOF mode of precipitation over East Asia. The red line is the same as the black line, but for the PC1 of precipitation over East Asia independent of EN. The 850-hPa wind (vectors; m s−1) regression field onto PC1 passing the 95% confidence level is also shown in (a) and (b). Purple lines in (a) and (b) represent the Yangtze River and the Yellow River. The gray dashed lines in (a) and (b) are 27°N and 127°E, which divide the East Asian monsoon domain into four subregions. The dots in (c) mark the years 1983, 1998, and 2016, which follow the super El Niño events.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

After removing the impact of ENSO, the spatial pattern of the leading mode of IEASMR and its variances indeed remain quite similar (Fig. 2b), and the PC1 of IEASMR is also largely unchanged. The first mode of ENSO-independent IEASMR is also significant and separated from other modes. It is interesting that after removing ENSO’s impact, changes of PC1 mainly occur after the super El Niño events such as those in 1982/83, 1997/98, and 2015/16 (Fig. 2c), suggesting that ENSO’s impact on IEASMR mainly relies on strong El Niño events (Wang et al. 2017). It should be noted that the correlation coefficient is 0.51 between EN and PC1 (p < 0.05), but it decreases to zero (Figs. S1c,d) after removing the impact of previous winter ENSO, indicating the reliability of the method for removing ENSO’s impact (Fig. 2c).

One may doubt that the existence of ENSO’s delay effect (via IOBM) could reduce the robustness of the above results. To clarify this issue, the EOF mode after removing IOBM’s impact (Fig. S3) is also calculated. The EOF mode of IOBM-independent IEASMR basically shows the same pattern as that of ENSO-independent IEASMR. The consistent result verifies the limited contribution of ENSO’s impact on IEASMR directly or indirectly.

One may also doubt that why the correlation coefficients with ENSO change so much (Fig. 2c) while the PC1 remains almost unchanged after removing ENSO’s impacts? The reason lies in the changes of a few important years in supporting the relation between the PC1 and EN index after removing ENSO’s impact, such as super El Niño years in 1983, 1998, and 2016 (Fig. S4). It further proves the viewpoint that only the super El Niño events can strongly affect the East Asian summer monsoon Wang et al. 2017).

To examine the dynamic and thermodynamic fields related to the ENSO-independent IEASMR, the tropospheric lower-to-upper circulation, SST, and precipitation are regressed onto PC1_noEN. As shown in Fig. 3, without the impact of ENSO, East Asia is dominated by a tripole meridional wave train, which resembles the Pacific–Japan pattern (PJ-pattern) or the East Asian–Pacific Pattern (EAP-pattern) (Nitta 1987; Huang and Sun 1992). A positive precipitation anomaly along the Yangtze River basin and southern Japan is jointly controlled by the NEAC and WNPAC, while a negative precipitation anomaly appears over the tropical western North Pacific, where merely the WNPAC dominates. At mid- to high latitudes over the Eurasian continent, an external Rossby wave (Held et al. 1985; Chen et al. 2013) in terms of equivalent barotropic wave train appears, which is characterized by six anomalous cyclones or anticyclones centered at Newfoundland Island, the northeastern subtropical Atlantic, northern Europe, the western Siberian basin, the central Siberian Plateau, and eastern Siberia. The wave activity flux at 500 hPa in Fig. 3b indicates that the Rossby wave train propagates from the North Atlantic to East Asia. Associated with the equivalent barotropic Rossby wave train, significant SSTAs appear over the Atlantic Ocean, with uniform warming over the tropical Atlantic and a “cold-west and warm-east” zonal dipole pattern over the extratropical North Atlantic. It is thus suggested that the equivalent barotropic wave train associated with IEASMR may be induced by the Atlantic SST anomalies.

Fig. 3.
Fig. 3.

Regressed boreal summer-mean (a) 200-hPa geopotential height (contours; gpm), wind (vectors; m s−1), and precipitation (shading; mm day−1) onto PC1_noEN. (b) As in (a), but for 500-hPa geopotential height (contours; gpm) and wave activity flux (pink vectors; m2 s−2). (c) As in (a), but for 850-hPa geopotential height (contours; gpm), wind (vectors; m s−1), and SST (shading; °C). The red (blue) dashed box in (c) indicates the key region with anomalous SST signals over the tropical (extratropical) Atlantic. The letters “A” and “C” indicate the centers of anticyclonic and cyclonic anomalies, respectively. Stippled areas represent values passing the 95% confidence level. The gray shading in (c) represents the Tibetan Plateau.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

In summary, the dynamic origins of IEASMR independent of ENSO can be traced back to both tropical Atlantic and extratropical North Atlantic SSTAs. Then, a question naturally arises: what are the physical processes behind the Atlantic SSTAs’ driving on IEASMR? In the following sections, we investigate the mechanisms for the influences of tropical Atlantic and extratropical North Atlantic SST on IEASMR, respectively.

4. Dynamic origins of IEASMR in Atlantic SSTAs

The above analysis suggests that the dynamic origins of IEASMR independent of ENSO may be rooted in Atlantic SST. To investigate the individual impacts of Atlantic SSTAs on IEASMR, we define two indices based on the significant SSTA signals (Fig. 3c). One is the tropical Atlantic SST index (TAI), which is the averaged SST over 20°S–15°N, 60°W–10°E; the other is the extratropical North Atlantic SST index (ETAI), which is the averaged SST over 30°–50°N, 50°–10°W minus that over 30°–50°N, 75°–50°W. Note that the definition of TAI and ETAI here are based on the SST fields after removing the ENSO’s impact. The correlation coefficient between TAI and ETAI is only 0.24 without passing the 95% confidence level, suggesting that SSTA over the tropical and the extratropical Atlantic are independent. With correlation coefficients of 0.35 and 0.33 (p < 0.05), both TAI and ETAI have significant relationships with PC1_noEN, but neither has a significant relationship with previous ENSO (Figs. S1e,f), suggesting again that the Atlantic SSTAs are the impact factors of ENSO-independent IEASMR. In the following subsections, the physical processes behind the tropical and extratropical Atlantic SSTAs’ forcings on IEASMR are investigated, respectively.

a. The forcing of tropical Atlantic SSTA

Figure 4 shows the atmospheric circulation, SST, and precipitation anomalies associated with TAI. With the positive SSTA over the tropical Atlantic, enhanced convection appears over the equatorial Atlantic due to the low-level convergence (Figs. 4a,c). To the west of the positive convection anomalies, an anomalous equatorial low-level westerly wind blows from 80°W to the Atlantic at 20°W. The zonal wind anomalies further intensify the warm (cold) SSTA over the tropical Atlantic (central/eastern Pacific) (Fig. 4c). The tropical zonal dipole pattern of SSTA results in the dipole convection pattern characterized by suppressed convection anomalies in the equatorial Pacific and enhanced convection anomalies in the equatorial Atlantic (Fig. 4a). The suppressed convection in the equatorial Pacific could induce the low-level WNPAC through the wind–evaporation–SST mechanism (Gill 1980; Wang et al. 2000), influencing IEASMR via the tropical route (Fig. 4c). This relay effect of eastern Pacific SSTA on WNPAC is consistent with previous studies (Ham et al. 2013; Chen et al. 2015).

Fig. 4.
Fig. 4.

As in Fig. 3, but for the regression onto TAI.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

Meanwhile, the enhanced convection (diabatic heating) in the tropical Atlantic could also lead to an upper anomalous anticyclone over the Bay of Mexico (Fig. 4a), which acts as the Rossby wave source and leads to an equivalent barotropic Rossby wave train. The Rossby wave train emanates from the Atlantic, propagates to northern Europe, and then bends to northeast Asia with a cyclonic anomaly (NEAC), thus influencing IEASMR via the extratropical route (Figs. 4b,c). This route is consistent with the result revealed in Lu et al. (2020) and Zhu et al. (2023).

To further illustrate that the negative SSTA over the tropical central/eastern Pacific is independent of ENSO, the averaged tropical SST is regressed on the TAI (Fig. S1e), and no Pacific SSTA signal can be found before the onset of Atlantic SSTA. The onset of Pacific SST cooling in April follows the warm Atlantic SSTA that begins from January, suggesting that the tropical Atlantic SSTA is independent of ENSO, and the Pacific SSTA could be induced by the Atlantic SSTA via teleconnections (Rodríguez‐Fonseca et al. 2009; Ding et al. 2012, 2013; Keenlyside et al. 2013; Ham et al. 2013; Chen et al. 2015; Polo et al. 2015; Jiang and Li 2021).

With the aim of verifying that the tropical Atlantic SSTA could remotely impact IEASMR via both tropical and extratropical routes, three numerical simulations are conducted with AGCM (Fig. 5). The first simulation (Exp_TA + TP) is run by prescribed positive/negative atmospheric heating with the horizontal pattern consistent with the observed zonal dipole convection pattern in the equatorial Pacific and Atlantic (Fig. 4a). The second simulation (Exp_TA) is run by prescribed positive diabatic heating only in the equatorial Atlantic, whereas the third one (Exp_TP) is run by the prescribed negative diabatic heating only in the equatorial central/eastern Pacific. Different heating rates in different layers are given (Fig. S5a) in the model to mimic the vertical heating profile in observation.

Fig. 5.
Fig. 5.

The (a) 200- and (b) 850-hPa geopotential height (contours; gpm) and wind (vectors; m s−1) response to the positive atmospheric heating over the TA and negative atmospheric heating over the TP. (c),(d) As in (a) and (b), but for the response to the positive atmospheric heating over TA only. (e),(f) As in (a) and (b), but for the response to the negative atmospheric heating over TP only. The letters “A” and “C” indicate the centers of anticyclonic and cyclonic anomalies, respectively.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

Figure 5 shows the equilibrium state of the lower- and upper-level circulation responses to different diabatic heating in AGCM. When positive diabatic heating in the Atlantic and negative diabatic heating in the Pacific are both given (Exp_TA + TP), an equivalent barotropic Rossby wave train consistent with observation is stimulated emanating from the North Atlantic to northeast Asia. The NEAC could be nicely reproduced, which confirms the independent influence of tropical Atlantic SSTA on IEASMR via the extratropical route (Figs. 4a, 5a). Meanwhile, according to the Gill’s solution (Gill 1980; Hoskins and Karoly 1981), the upper-level (lower-level) cyclonic (anticyclonic) anomaly is induced as a tropical Rossby wave response over the western North Pacific to the northwest of the negative diabatic heating in the tropical Pacific (TP), whereas the easterly anomaly with poleward declining is induced as a Kelvin wave response (Figs. 5a,b) to the east of the positive diabatic heating in the Atlantic. Both induce the WNPAC and influence IEASMR via the tropical routes.

When only positive diabatic heating over the tropical Atlantic is prescribed, a downstream Rossby wave train is still induced with circulation centers consistent with the observation (Figs. 5c and 4a). Therefore, the NEAC of the Rossby wave train could influence IEASMR via the extratropical route. Meanwhile, strong lower-level easterlies declining poleward appear from 0° to 150°E, which enhances the WNPAC in the absence of Pacific negative diabatic heating (Fig. 5d) through Kelvin wave response. Note that the lower-level easterlies in the Indian Ocean cannot be observed (Fig. 4d) because of another positive diabatic heating over Maritime Continent. The westerlies induced by the positive diabatic heating could offset the easterlies of the TAI-related Kelvin wave response. Figure 5e shows the 200-hPa circulation response merely to the negative diabatic heating over the tropical Pacific. The negative diabatic heating over the central/eastern equatorial Pacific could induce the Pacific–North American (PNA)–like pattern, but no significant teleconnection response can be found over the Eurasian continent, suggesting that the TP cooling cannot influence IEASMR from the extratropical route. However, the WNPAC response to TP cooling resembles the observation (Figs. 4c, 5f), indicating that the TP cooling induced by positive tropical Atlantic SSTA could still influence IEASMR via the tropical route (Ham et al. 2013).

In sum, it can be concluded from both observational diagnosis and numerical experiments that tropical Atlantic SSTA could influence the IEASMR, which is independent of ENSO. Positive tropical Atlantic SSTA induces a dipole convection anomaly pattern over the equatorial central/eastern Pacific and tropical Atlantic. The suppressed convection over the Pacific influences WNPAC through tropical Rossby wave response, while the enhanced convection over the TA enhanced NEAC/WNPAC via both extratropical and tropical routes.

b. The forcing of extratropical North Atlantic SSTA

After demonstrating the tropical Atlantic SSTA’s forcing on IEASMR, we now turn to investigate how the extratropical North Atlantic SSTA impacts IEASMR. Figure 6 shows the atmospheric circulation, precipitation, and SST fields regressed onto ETAI. Corresponding to the dipole SSTA pattern over the North Atlantic, a pair of barotropic cyclonic and anticyclonic anomalies appear above the cold SSTA in the western North Atlantic and the warm SSTA in the eastern North Atlantic, respectively. To the east of the cyclone–anticyclone pair, another five barotropic circulation anomalies can be found over the Eurasian continent, forming an equivalent barotropic Rossby wave train. The Rossby wave activity flux (Fig. 6b) indicates that the Rossby wave train emanates from the North Atlantic and spreads downstream to East Asia, impacting IEASMR.

Fig. 6.
Fig. 6.

As in Fig. 3, but for the regression onto ETAI.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

To further explain how the dipole SSTA pattern induces the dipole geopotential pattern, the local zonal vertical profiles of the regressed geopotential height and air temperature anomalies onto ETAI averaged over 30°–50°N are calculated (Fig. S6). The negative SSTA to the west of the extratropical North Atlantic cools down the atmosphere in situ through negative net heat flux, causing the shrinking of the air column and therefore the cyclonic (low pressure) anomaly. Meanwhile, the positive SSTA to the east of the extratropical North Atlantic warms up the atmosphere in situ through positive net heat flux, leading to the expansion of the air column and therefore the anticyclonic (high pressure) anomaly (Fig. S6). The pair of circulation anomalies perturb the westerly jet stream, inducing the equivalent barotropic Rossby wave train.

To further confirm the above hypothesis on the extratropical North Atlantic SST forcing on IEASMR, a numerical simulation with prescribed atmospheric heating related to the extratropical SSTAs is conducted using an AGCM. Since the SSTAs over the subtropical North Atlantic do not induce convective heating, the maximum atmospheric heating/cooling rate of 1.0 K day−1 is prescribed at σ = 0.9 (approximately 850-hPa), and it is gradually reduced with height to mimic the observed heating profile (Fig. S5b).

As shown in Fig. 7, the dipole anomalous atmospheric circulation can be stimulated through the dipole heating anomalies, which is consistent with the observation. An equivalent barotropic Rossby wave train with five circulation anomalies appears over the Eurasia Continent, ending with the cyclonic anomaly over northeast Asia (NEAC). The result of the numerical experiment has confirmed that the extratropical North Atlantic SST forcing could modulate northeast Asian circulation anomalies and influence IEASMR by inducing a barotropic Rossby wave train across the Eurasian continent.

Fig. 7.
Fig. 7.

The 200-hPa geopotential height (contours; gpm) and wind (vectors; m s−1) response to the atmospheric heating over the extratropical North Atlantic. The letters “A” and “C” indicate the centers of anticyclonic and cyclonic anomalies, respectively.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

In sum, from observation analysis and numerical experiments, the tropical Atlantic and extratropical northern Atlantic SSTAs could both influence the ENSO-independent IEASMR by inducing atmospheric teleconnections. On one hand, positive SSTA over the tropical Atlantic induces local diabatic heating, which triggers a mid- to high-latitude Rossby wave train emanating from the Atlantic, across the Eurasia continent, and ending with NEAC, influencing the IEASMR via an extratropical route. In addition, the tropical Atlantic SSTA-induced zonal dipole diabatic heating over tropical Pacific and Atlantic could also influence IEASMR via different tropical routes. On the other hand, the dipole SSTA pattern over extratropical North Atlantic induces local circulation anomalies through heat flux exchange, which further triggers a mid- to high-latitude Rossby wave train, modulating the IEASMR via another extratropical route.

5. Hindcast of IEASMR by adding Atlantic predictability source

Thus far, we have concluded that the dynamic origins of the ENSO-independent IEASMR can be traced back to the tropical Atlantic and extratropical North Atlantic. Could the SSTAs over these Atlantic regions be the predictors of IEASMR? The identified simultaneous Atlantic SSTA forcings are unable to serve as predictors. Therefore, to detect the precursors of the Atlantic SSTA forcings of IEASMR that are independent of ENSO, the evolution of the Atlantic SST regressed on TAI and ETAI from previous winter to summer is examined. As shown in Fig. 8a, the anomalous tropical Atlantic SST warming in summer can be traced back to the positive SSTA over the tropical South Atlantic during the previous winter (Fig. S1e). The positive SSTA keeps inducing an anomalous equatorial westerly wind, intensifying the warm SSTA near the equator during spring, and the positive tropical Atlantic SSTA peaks in summer (Figs. 8b,c), influencing IEASMR via both tropical and extratropical routes (Figs. 4, 5).

Fig. 8.
Fig. 8.

Regressed 850-hPa geopotential height (contours; gpm), wind (vectors; m s−1), and SST (shading; °C) during (a) winter, (b) spring, and (c) summer onto TAI. (d),(e),(f) As in (a), (b), and (c), but for ETAI. The gray dashed box indicates the region for calculating the PTA and PETA indices. Stippled areas are values passing the 95% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

For the extratropical North Atlantic SSTA dipole, during the previous winter, a low-level cyclonic anomaly dominates the entire extratropical North Atlantic (Fig. 8d). The northerly (southerly) wind to its west (east) advects cold (warm) water to the south (north) (Fig. 8e). Thus, a zonal SSTA dipole is further developed and begins to play an active role in driving the atmospheric circulation in situ during summer (Fig. 8f). Therefore, the zonal dipole SSTA pattern appears in summertime and influences IEASMR by inducing the downstream Rossby wave train (Fig. 6).

The evolution of Atlantic SSTAs indicates that the SSTAs over the tropical Atlantic and extratropical North Atlantic have precursors; therefore, their contribution as the predictability source can be compared with that of ENSO. The positive SSTA averaged over 30°S–5°N, 60°W–20°E (gray dashed line in Fig. 8a) during DJF is defined as the precursor of tropical Atlantic SSTA (PTA), whereas the positive SSTA minus the negative SSTA over 10°–45°N, 65°–10°W (gray dashed line in Fig. 8d) during DJF is regarded as the precursor of extratropical North Atlantic SSTA (PETA).

With the precursors of the Atlantic SSTA forcings of ENSO-independent IEASMR (PC1_noEN), the hindcast of IEASMR (PC1) is conducted through multiple regression using Niño-3.4 and PTA/PETA indices. The fractional variance of the total IEASMR explained by the reconstructed precipitation using the hindcast PC1 and EOF pattern is shown in Fig. 9a. Compared with the fractional variances explained by ENSO only, the variance increases to 14.67% over the tropical western North Pacific and to 9.91% over the South China Sea after adding the Atlantic precursors, suggesting the impact of tropical Atlantic SST on IEASMR via the tropical routes (the tropical Kelvin wave response of TA positive heating and the tropical Rossby wave response of TP negative heating). To the north of 27°N, where the NEAC controls, the variance doubles over eastern China (Fig. 1), verifying the indispensable role of tropical Atlantic and extratropical North Atlantic SSTAs on NEAC and IEASMR (also see Fig. S7). It is worth noting that the correlation coefficients of PC1 with PTA and EN are both 0.51 (p < 0.05) (Fig. 9b), indicating the comparable contributions from tropical Atlantic SSTA and ENSO. In addition, the correlation coefficient between PC1 and PETA is 0.28, passing the 90% confidence level.

Fig. 9.
Fig. 9.

(a) Distribution of the fractional variance of rainfall reconstructed through the combination of ENSO (Niño-3.4 index) and independent Atlantic SSTAs (PTA and PETA indices). (b) Scatterplots between the observed PC of IEASMR and the EN (blue dots), PTA (green dots), and PETA (orange dots). (c) As in (b), but for the observed PC of IEASMR and the reconstructed PC based on EN (blue dots), PTA, and PETA (red dots) and EN, PTA, and PETA (purple dots). Purple lines in (a) represent the Yangtze River and Yellow River. The gray dashed lines in (a) are 27°N and 127°E, which divide the four subregions of East Asia with their averaged variances shown on the top left of each subregion. Colored numbers in (b) and (c) represent the corresponding correlation coefficients.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

Figure 9c shows the reconstructed PC1 based on the ENSO predictor (EN), the Atlantic SSTA predictors (PTA and PETA), and all the predictors, respectively. The PC1 reconstructed by Atlantic SSTAs has a correlation coefficient of 0.54 with the original PC1 (p < 0.05), which is comparable with that between the PC1 reconstructed by EN and the original PC1 (0.51). For the PC1 reconstructed by all the predictors, the regression coefficients for EN, PTA, and PETA are 0.35, 0.35, and 0.14, respectively. The almost equivalent correlation coefficient and same regression coefficient have again confirmed that the contribution from tropical Atlantic SSTA to EAMSRI is comparable with that from ENSO. Although the contribution of extratropical North Atlantic SSTA is relatively lower, it still accounts for a significant portion. The correlation coefficient between the observed PC1 and the reconstructed PC1 by all predictors can reach 0.63 (p < 0.05), which obviously outperforms that reconstructed by EN only (0.51).

In sum, the Atlantic origins of ENSO-independent IEASMR are the persistent Atlantic SST signals. After adding the precursors of the Atlantic origins, the multiple regression model yields much better prediction skill compared with that merely using ENSO (increased from 0.51 to 0.63), and the fractional variances over the majority of the East Asian monsoon domain are largely increased.

6. Summary and discussion

a. Conclusions

The ENSO-explained variance of the interannual variability of East Asian summer monsoon rainfall is very limited, particularly for that over the subtropical land (Fig. 1). To identify the impact factors of ENSO-independent IEASMR, the ENSO-related precipitation is linearly removed to look into the leading mode of IEASMR. It is found that the spatial pattern, PC, and explained variances of the leading mode all remain almost unchanged after the removal (Fig. 2), suggesting the existence of alternative impact factors and predictability sources of IEASMR.

The tropical Atlantic SSTA and the extratropical dipole SSTA pattern over the North Atlantic are found to be related to ENSO-independent IEASMR through atmospheric teleconnections. As summarized by the schematic diagram in Fig. 10, positive SSTA over the tropical Atlantic leads to positive diabatic heating over the tropical Atlantic and negative diabatic heating over the tropical Pacific. The positive diabatic heating over the tropical Atlantic triggers a mid- to high-latitude Rossby wave train from the Atlantic to East Asia and thus induces the northeast Asian anomalous cyclone via the extratropical route. In addition, the positive (negative) diabatic heating over tropical Atlantic (Pacific) could induce the WNPAC in terms of Kelvin (Rossby) wave response via tropical routes. The extratropical route ends with the NEAC, whereas the tropical routes end with the WNPAC, both of which could significantly influence IEASMR (Fig. 10a). The dipole SSTA pattern over the extratropical North Atlantic with cooling in the west and warming in the east could induce a dipole circulation anomaly pattern therein through heat flux exchange. The pair of circulation anomalies perturbs the westerly jet stream, inducing the equivalent barotropic Rossby wave train, which ends with a cyclonic anomaly over northeast Asia and thus modulates IEASMR (Fig. 10b). To simulate the circulation response to the tropical Atlantic and extratropical North Atlantic SSTA forcings, diabatic heating with a different vertical profile consistent with the observation is prescribed in the numerical model. The simulations confirm the tropical and extratropical routes by which Atlantic SSTAs impact ENSO-independent IEASMR.

Fig. 10.
Fig. 10.

Schematic diagram of (a) the tropical Atlantic and (b) extratropical North Atlantic SSTA forcings on IEASMR. Red (blue) shading denotes the positive (negative) SSTAs. Contours in (a) represent precipitation anomalies related to the tropical Atlantic forcing. Light blue dashed lines with arrows are the two pathways of the Rossby wave train induced by SSTAs over the tropical Atlantic and extratropical North Atlantic. The solid blue (red) circles with arrows indicate barotropic anticyclonic (cyclonic) anomalies. The dashed blue circle represents the 850-hPa cyclonic anomaly, and the solid purple circle represents the 200-hPa anticyclonic anomaly. The black dashed vector indicates the 850-hPa zonal wind response to the tropical Atlantic SSTA forcing. The letters “A” and “C” indicate the centers of anticyclonic and cyclonic anomalies, respectively.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0061.1

The preceding evolution of the tropical Atlantic SSTA and the extratropical North Atlantic SSTA dipole is further examined to search the predictability sources of IEASMR. The tropical Atlantic SSTA can be traced back to the positive SSTA over the tropical South Atlantic during the previous winter, whereas the zonal SSTA dipole over extratropical North Atlantic originates from the local atmospheric forcing the previous winter and spring. By adding the winter (DJF) precursors of the Atlantic SSTA forcings to the empirical prediction model, the prediction skill of the correlation coefficient for the PC1 of IEASMR has been improved from 0.51 to 0.63 (p < 0.05), and the explained fractional variance of the total rainfall over the East Asian monsoon domain has been increased, particularly for eastern China.

b. Discussion

Different from previous studies, which only emphasize the role of the ENSO-induced WNPAC in driving the interannual variability of East Asian summer monsoon rainfall (Zhang et al. 1996; Wang et al. 2000; Xie et al. 2009), the present study underpins the contribution of NEAC to ENSO-independent IEASMR. The NEAC is usually manifested as the cold vortex over northeast China (Zhang et al. 2008; Xie and Bueh 2017) on the synoptic scale, and it acts as an accomplice to the WNPAC for IEASMR. Both tropical and extratropical Atlantic SSTAs could significantly modulate the IEASMR by inducing tropical and extratropical atmospheric teleconnections.

The results herein could advance our understanding of the ENSO–monsoon relationship and improve the seasonal prediction skill of IEASMR as well as inspire us to pay more attention to Atlantic SSTA forcings. Note that the IEASMR–Atlantic SST relationship may undergo interdecadal changes, but it could not influence the new finding in this paper that both tropical and extratropical Atlantic SSTA could exert their impact on IEASMR apart from ENSO.

Many previous studies have demonstrated the linkage between tropical Atlantic SST and WNPAC (Hong et al. 2015; Chen et al. 2015; Zuo et al. 2020) or ENSO events (Ham et al. 2013; Jiang and Li 2019, 2021), and the essential role of Atlantic oceanic forcing in driving EASMR and WNAPC was also unraveled from a perspective of paleoclimate (Chiang et al. 2015; He et al. 2021a,b). However, these studies mainly focused on the Atlantic oceanic forcing from tropical routes. This study underpins the extratropical routes from the tropical/extratropical Atlantic in influencing the East Asian summer monsoon rainfall. The Atlantic SST anomalies associated with the ENSO-independent IEASMR are quite different from that with the original IEASMR (Fig. S8).

Note that the origins of the positive SSTA over the tropical South Atlantic and the cyclonic anomaly over the subtropical North Atlantic during boreal winter (Jiang and Li 2019) remain elusive. In addition, as shown in Fig. 6b, the observed cyclonic anomaly over central Russia along the Rossby wave train is much stronger and closer to the polar region compared with that in the simulation (Fig. 7), and the wave activity flux can be seen over the upstream polar region. Can the boundary layer forcing over the Arctic region independently affect IEASMR? What is the relative importance of these independent factors in influencing IEASMR? These unresolved issues merit further exploration.

Acknowledgments.

This work was supported by the National Natural Science Foundation of China (Grants 42088101 and 42175033) and the High-Performance Computing Center of Nanjing University of Information Science and Technology.

Data availability statement.

The observed monthly precipitation is provided by the Global Precipitation Climatology Project at https://psl.noaa.gov/data/gridded/data.gpcp.html. The monthly sea surface temperature data from the Hadley Centre Sea Ice and Sea Surface Temperature data set is available at https://www.metoffice.gov.uk/hadobs/hadsst2/data/download.html. All monthly atmospheric circulation and air temperature data provided by the National Centers for Environmental Prediction reanalysis are openly available at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.

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  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

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  • Chen, G., R. Huang, and L. Zhou, 2013: Baroclinic instability of the Silk Road pattern induced by thermal damping. J. Atmos. Sci., 70, 28752893, https://doi.org/10.1175/JAS-D-12-0326.1</